TIME SERIES ARIMA MODEL FOR PREDICTION OF THAILAND’S MONTHLY AVERAGE CASSAVA STARCH DOMESTIC PRICE

2020 ◽  
Vol 63 (2) ◽  
pp. 191-205
Author(s):  
Saowapa Chaipitak
Keyword(s):  
Time Series ◽  
Arima Model ◽  
Cassava Starch ◽  
Domestic Price ◽  
Monthly Average

scholarly journals The Comparison between ARIMA and ARFIMA Model to Forecast Kijang Emas (Gold) Prices in Malaysia using MAE, RMSE and MAPE

2021 ◽  
Vol 6 (3) ◽  
pp. 22-33
Author(s):  
Atiqa Nur Azza Mahmad Azan ◽  
Nur Faizatul Auni Mohd Zulkifly Mototo ◽  
Pauline Jin Wee Mah
Keyword(s):  
Time Series ◽  
Arima Model ◽  
Time Series Models ◽  
Monthly Average ◽  
Arfima Models ◽  
Gold Market ◽  
Univariate Time Series ◽  
The World ◽  
Arfima Model ◽  
Best Fit

Gold is known as the most valuable commodity in the world because it is a universal currency recognized by every single bank across the globe. Thus, many people were interested in investing gold since gold market was always steadier compared to other investment (Khamis and Awang, 2020). However, the credibility of gold was questionable due to the changes in gold prices caused by a variety of circumstances (Henriksen, 2018). Hence, information on the inflation of gold prices were needed to understand the trend in order to plan for the future in accordance with international gold price standards. The aim of this study was to identify the trend of Kijang Emas monthly average prices in Malaysia from the year 2010 to 2021, to determine the best fit time series model for Kijang Emas prices in Malaysia and using univariate time series models to forecast Kijang Emas prices in Malaysia. The ARIMA and ARFIMA models were used in this study to model and forecast the prices of gold (Kijang Emas) in Malaysia. Each of the actual monthly Kijang Emas prices for 2021 were found to be within the 95% predicted intervals for both the ARIMA and ARFIMA models. The performances for each model were checked by considering the values of MAE, RMSE and MAPE. From the findings, all the MAE, RMSE and MAPE values showed that the ARFIMA model emerged as the better model in forecasting the Kijang Emas prices in Malaysia compared to the ARIMA model.

scholarly journals Hybrid Model for Time Series of Complex Structure with ARIMA Components

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1122
Author(s):  
Oksana Mandrikova ◽  
Nadezhda Fetisova ◽  
Yuriy Polozov
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hybrid Model ◽  
Complex Structure ◽  
Arima Model ◽  
Ionospheric Parameter ◽  
Arima Models ◽  
Complicated Structure ◽  
Threshold Functions ◽  
Anomalous Component

A hybrid model for the time series of complex structure (HMTS) was proposed. It is based on the combination of function expansions in a wavelet series with ARIMA models. HMTS has regular and anomalous components. The time series components, obtained after expansion, have a simpler structure that makes it possible to identify the ARIMA model if the components are stationary. This allows us to obtain a more accurate ARIMA model for a time series of complicated structure and to extend the area for application. To identify the HMTS anomalous component, threshold functions are applied. This paper describes a technique to identify HMTS and proposes operations to detect anomalies. With the example of an ionospheric parameter time series, we show the HMTS efficiency, describe the results and their application in detecting ionospheric anomalies. The HMTS was compared with the nonlinear autoregression neural network NARX, which confirmed HMTS efficiency.

scholarly journals A Bayesian Model to Forecast the Time Series Kinetic Energy Data for a Power System

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3299
Author(s):  
Ashish Shrestha ◽  
Bishal Ghimire ◽  
Francisco Gonzalez-Longatt
Keyword(s):  
Time Series ◽  
Kinetic Energy ◽  
Power System ◽  
Bayesian Model ◽  
Arima Model ◽  
Power Converter ◽  
Series Data ◽  
Percentage Error ◽  
Optimal Size ◽  
Short Term

Withthe massive penetration of electronic power converter (EPC)-based technologies, numerous issues are being noticed in the modern power system that may directly affect system dynamics and operational security. The estimation of system performance parameters is especially important for transmission system operators (TSOs) in order to operate a power system securely. This paper presents a Bayesian model to forecast short-term kinetic energy time series data for a power system, which can thus help TSOs to operate a respective power system securely. A Markov chain Monte Carlo (MCMC) method used as a No-U-Turn sampler and Stan’s limited-memory Broyden–Fletcher–Goldfarb–Shanno (LM-BFGS) algorithm is used as the optimization method here. The concept of decomposable time series modeling is adopted to analyze the seasonal characteristics of datasets, and numerous performance measurement matrices are used for model validation. Besides, an autoregressive integrated moving average (ARIMA) model is used to compare the results of the presented model. At last, the optimal size of the training dataset is identified, which is required to forecast the 30-min values of the kinetic energy with a low error. In this study, one-year univariate data (1-min resolution) for the integrated Nordic power system (INPS) are used to forecast the kinetic energy for sequences of 30 min (i.e., short-term sequences). Performance evaluation metrics such as the root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean absolute scaled error (MASE) of the proposed model are calculated here to be 4.67, 3.865, 0.048, and 8.15, respectively. In addition, the performance matrices can be improved by up to 3.28, 2.67, 0.034, and 5.62, respectively, by increasing MCMC sampling. Similarly, 180.5 h of historic data is sufficient to forecast short-term results for the case study here with an accuracy of 1.54504 for the RMSE.

scholarly journals Identification of monthly municipal water demand system based on autoregressive integrated moving average model tuned by particle swarm optimization

2017 ◽  
Vol 19 (2) ◽  
pp. 261-281 ◽  
Author(s):  
Sahbi Boubaker
Keyword(s):  
Time Series ◽  
Particle Swarm Optimization ◽  
Water Demand ◽  
Moving Average ◽  
Arima Model ◽  
Particle Swarm ◽  
Demand System ◽  
Swarm Optimization ◽  
Municipal Water ◽  
Average Absolute Relative Error

In this paper, a modeling-identification approach for the monthly municipal water demand system in Hail region, Saudi Arabia, is developed. This approach is based on an auto-regressive integrated moving average (ARIMA) model tuned by the particle swarm optimization (PSO). The ARIMA (p, d, q) modeling requires estimation of the integer orders p and q of the AR and MA parts; and the real coefficients of the model. More than being simple, easy to implement and effective, the PSO-ARIMA model does not require data pre-processing (original time-series normalization for artificial neural network (ANN) or data stationarization for traditional stochastic time-series (STS)). Moreover, its performance indicators such as the mean absolute percentage error (MAPE), coefficient of determination (R2), root mean squared error (RMSE) and average absolute relative error (AARE) are compared with those of ANN and STS. The obtained results show that the PSO-ARIMA outperforms the ANN and STS approaches since it can optimize simultaneously integer and real parameters and provides better accuracy in terms of MAPE (5.2832%), R2 (0.9375), RMSE (2.2111 × 105m3) and AARE (5.2911%). The PSO-ARIMA model has been implemented using 69 records (for both training and testing). The results can help local water decision makers to better manage the current water resources and to plan extensions in response to the increasing need.

Towards optimal ATM cash replenishment using time series analysis

2021 ◽  
pp. 1-13
Author(s):  
Muhammad Rafi ◽  
Mohammad Taha Wahab ◽  
Muhammad Bilal Khan ◽  
Hani Raza
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Automatic Teller Machine ◽  
Proposed Model ◽  
Auto Regressive ◽  
Specific Amount

Automatic Teller Machine (ATM) are still largely used to dispense cash to the customers. ATM cash replenishment is a process of refilling ATM machine with a specific amount of cash. Due to vacillating users demands and seasonal patterns, it is a very challenging problem for the financial institutions to keep the optimal amount of cash for each ATM. In this paper, we present a time series model based on Auto Regressive Integrated Moving Average (ARIMA) technique called Time Series ARIMA Model for ATM (TASM4ATM). This study used ATM back-end refilling historical data from 6 different financial organizations in Pakistan. There are 2040 distinct ATMs and 18 month of replenishment data from these ATMs are used to train the proposed model. The model is compared with the state-of- the-art models like Recurrent Neural Network (RNN) and Amazon’s DeepAR model. Two approaches are used for forecasting (i) Single ATM and (ii) clusters of ATMs (In which ATMs are clustered with similar cash-demands). The Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) are used to evaluate the models. The suggested model produces far better forecasting as compared to the models in comparison and produced an average of 7.86/7.99 values for MAPE/SMAPE errors on individual ATMs and average of 6.57/6.64 values for MAPE/SMAPE errors on clusters of ATMs.

scholarly journals Research on GDP Forecast Analysis Combining BP Neural Network and ARIMA Model

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shaobo Lu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Error Correction ◽  
Relative Error ◽  
Bp Neural Network ◽  
Arima Model ◽  
Forecast Model ◽  
Price Prediction ◽  
The Neural Network

Based on the BP neural network and the ARIMA model, this paper predicts the nonlinear residual of GDP and adds the predicted values of the two models to obtain the final predicted value of the model. First, the focus is on the ARMA model in the univariate time series. However, in real life, forecasts are often affected by many factors, so the following introduces the ARIMAX model in the multivariate time series. In the prediction process, the network structure and various parameters of the neural network are not given in a systematic way, so the operation of the neural network is affected by many factors. Each forecasting method has its scope of application and also has its own weaknesses caused by the characteristics of its own model. Secondly, this paper proposes an effective combination method according to the GDP characteristics and builds an improved algorithm BP neural network price prediction model, the research on the combination of GDP prediction model is currently mostly focused on the weighted form, and this article proposes another combination, namely, error correction. According to the price characteristics, we determine the appropriate number of hidden layer nodes and build a BP neural network price prediction model based on the improved algorithm. Validation of examples shows that the error-corrected GDP forecast model is also better than the weighted GDP forecast model, which shows that error correction is also a better combination of forecasting methods. The forecast results of BP neural network have lower errors and monthly prices. The relative error of prediction is about 2.5%. Through comparison with the prediction results of the ARIMA model, in the daily price prediction, the relative error of the BP neural network prediction is 1.5%, which is lower than the relative error of the ARIMA model of 2%.

scholarly journals MATHEMATICAL MODELLING AS AN ELEMENT OF PLANNING RAIL TRANSPORT STRATEGIES

Transport ◽  
2021 ◽  
Vol 36 (4) ◽  
pp. 354-363
Author(s):  
Anna Borucka ◽  
Dariusz Mazurkiewicz ◽  
Eliza Łagowska
Keyword(s):  
Time Series ◽  
Development Strategy ◽  
Time Series Data ◽  
Arima Model ◽  
Exponential Smoothing ◽  
Rail Transport ◽  
Transport Infrastructure ◽  
Rolling Stock ◽  
Series Data ◽  
Passenger Transport

Effective planning and optimization of rail transport operations depends on effective and reliable forecasting of demand. The results of transport performance forecasts usually differ from measured values because the mathematical models used are inadequate. In response to this applicative need, we report the results of a study whose goal was to develop, on the basis of historical data, an effective mathematical model of rail passenger transport performance that would allow to make reliable forecasts of future demand for this service. Several models dedicated to this type of empirical data were proposed and selection criteria were established. The models used in the study are: the seasonal naive model, the Exponential Smoothing (ETS) model, the exponential smoothing state space model with Box–Cox transformation, ARMA errors, trigonometric trend and seasonal components (TBATS) model, and the AutoRegressive Integrated Moving Average (ARIMA) model. The proposed time series identification and forecasting methods are dedicated to the processing of time series data with trend and seasonality. Then, the best model was identified and its accuracy and effectiveness were assessed. It was noticed that investigated time series is characterized by strong seasonality and an upward trend. This information is important for planning a development strategy for rail passenger transport, because it shows that additional investments and engagement in the development of both transport infrastructure and superstructure are required to meet the existing demand. Finally, a forecast of transport performance in sequential periods of time was presented. Such forecast may significantly improve the system of scheduling train journeys and determining the level of demand for rolling stock depending on the season and the annual rise in passenger numbers, increasing the effectiveness of management of rail transport.

scholarly journals Coronavirus (COVID-19) Pandemic in Indonesia: Cases Overview and Daily Data Time Series using Naïve Forecast Method

2021 ◽  
Vol 3 (1) ◽  
pp. 1-8
Author(s):  
Annisa Puspa Kirana ◽  
Adhitya Bhawiyuga
Keyword(s):  
Time Series ◽  
Percentage Error ◽  
Forecast Method ◽  
Monthly Basis ◽  
Average Case ◽  
Monthly Average ◽  
Daily Data ◽  
Time Series Dataset ◽  
The World ◽  
Forecasting Analysis

At the end of December 2019, the virus emerges from Wuhan, China, and resulted in a severe outbreak in many cities in China and expanding globally, including Indonesia. Indonesia is the fourth most populated country globally. As of February 2021, Indonesia in the first rank of positive cases of COVID-19 in Southeast Asia, number 4 in Asia, and number 19 in the world. Our paper aims to provide detailed reporting and analysis of the COVID-19 case overview and forecasting that have hit Indonesia. Our time-series dataset from March 2020 to January 2021. Summary of cases studied included the number of positive cases and deaths due to COVID-19 on a daily or monthly basis. We use time series and forecasting analysis using the Naïve Forecast method.  The prediction is daily case prediction for six months starting from February 1, 2021, to June 30, 2021, using active cases daily COVID-19 data in all provinces in Indonesia. The highest monthly average case prediction is in June, which is 35,662 cases. Our COVID-19 prediction study has a mean absolute percentage error (MAPE) score of 15.85%.

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